Spatial heterogeneity for environmental performance and resilient behavior in energy and transportation systems

Fonseca, Jimeno A.; Mauriz, Laura Estévez; Forgaci, Claudiu; Björling, Nils

doi:10.1016/j.compenvurbsys.2016.11.001

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…However, most of the articles are not suitable for the interest of this study (see Table 3). For example, Fonseca et al [58] describe the concept of transportation resilience indicator, which assesses the ability of an urban system to reduce the number of people affected by traffic noise and to reduce the economic cost for transportation noise. Resilience refers to the ability of a system to maintain its state facing shocks.…”

Section: Screening Results and Relativity Analysismentioning

confidence: 99%

“…The shocks on TIs and the abilities of TIs to shocks are necessary conditions for the concept of TIs resilience [27]. Therefore, the indicators presented by Fonseca et al [58] are for assessing the resilience of the urban economic system to the effects of transportation, rather than the resilience of TIs. Moreover, Cariolet et al [13] assess the resilience of urban areas to traffic-related air pollution by indicators.…”

Section: Screening Results and Relativity Analysismentioning

confidence: 99%

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Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

et al. 2022

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Urban transport infrastructures (TIs) play a central role in an urban society that faces more and more disasters. TIs, part of critical infrastructures (CIs), are highly correlated with urban disaster management in terms of their resilience when cities are facing a crisis or disaster. According to many studies, indicator assessment has been frequently used for the resilience management of CIs in recent decades. Defining and characterizing indicators can be useful for disaster managers as it could help monitor and improve the capacities and performance of TIs. The purpose of this paper, therefore, is (1) to identify and summarize the existing indicators of TIs resilience from the currently available literature, and (2) to discuss the possible future studies of the resilience indicator of TIs. The first results indicated that there are some barriers to identify indicators following the common search method through keywords. Additionally, the indicators found are mainly related to technical information, the disruption stage, and internal TIs. Finally, due to the complexity of indicator assessment, sub-indicators and indicator spatialization are widely used in the resilience assessment of urban TIs studies.

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Section: Screening Results and Relativity Analysismentioning

confidence: 99%

Section: Screening Results and Relativity Analysismentioning

confidence: 99%

Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

et al. 2022

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“…Garc ıa-Onetti et al (2018). Fonseca et al (2017) deemed to establish bridges between the environmental performance and resilience for the logistics infrastructures. In addition, Cutter et al (2008) also suggested considering environmental performance as a component of resilience for the logistics infrastructures.…”

Section: Literature Review: Domains and Dimensions Of Environmental Performance And Resilience In Logistics Infrastructurementioning

confidence: 99%

“…(UNCTAD, 2014). Transport planners need new knowledge about the impact of any future adverse events or disruptions on the environmental performance and resilience of critical logistics infrastructures (Fonseca et al , 2017). Since there is a growing awareness of susceptibility in the international supply chains, the productivity of freight logistics communities increasingly relies on the undisturbed functioning of these logistics infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Kayıkçı

2020

JEIM

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PurposeAs the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.Design/methodology/approachA causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.FindingsThe result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.Originality/valueIn this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

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“…Optimization is achieved by parametric studies although a coupling with, e.g., genetic algorithms was mentioned as a potential extension. Fonseca et al (2017) use their tool CEA to examine the interactions between mixture of buildings' occupancy types, environmental performance in terms of emissions and noise pollution, and resilience of both energy and transport infrastructure. They cover sub-building scales with, e.g., the calculation of user densities up to the scale of a new neighborhood of 25 ha.…”

Section: State-of-the-artmentioning

confidence: 99%

The Optimum Is Not the Goal: Capturing the Decision Space for the Planning of New Neighborhoods

Schüler

Cajot

Peter

et al. 2018

Front. Built Environ.

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This article presents the development and application of a methodology that employs optimization not to seek the single or few optimal plan(s) but to provide planners with a systematic overview of their decision space. As urban development projects are not only subject to decisions of planners but also to those of many actors, the insight about how different actors would decide based on the decisions of the planners should enable planners to already adapt their decisions to ensure that final project targets are reached. The existence of different decision makers is considered via a multiparametric mixedinteger linear programming (mpMILP) approach. Along with the methodology a model was developed, which incorporates multiple domains and scales. Those domains include social, environmental, form, energy, and economic aspects. The considered scales range from single floors up to a neighborhood. Model and methodology were applied to a greenfield development project. Two practical questions are answered, which address the impact of planning decisions about (a) the building density and the sustainability of the energy supply on costs of different actors and (b) the building density and the share of parks on the view on a landmark. The capturing of the decision space revealed trade-offs in terms of chosen energy supply system and urban form, respectively. The presented computational method forms part of the decision support tool URB io , which shall assist urban planning.

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Spatial heterogeneity for environmental performance and resilient behavior in energy and transportation systems

Cited by 17 publications

References 11 publications

Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

The Optimum Is Not the Goal: Capturing the Decision Space for the Planning of New Neighborhoods

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